Considering Silences in Narrative Inquiry: An Intergenerational Story of a Sami Family
Bibliographic record
Abstract
Through coming alongside a Sami family, we open spaces to contemplate multiple forms of silence. We argue that rather than the antithesis to narrative, silence is an integral part of narrative inquiry. As narrative inquirers we need to be wakeful to what is told and also untold, often simultaneously. We believe that narrative inquiry is not necessarily about breaking silences, but it is also about honoring silences, as well as the practice of silence. By calling forward one author’s intergenerational experiences, we explore different aspects of silence such as silence as text, silence as context for living and telling, and silences following silencing. We explore how we live with, and within, silences, and how our told and untold stories are shaped by silences and, in turn, also shape silences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.026 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".